JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks
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DOI：
10.1145/3534678.3539286
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Jian Kang;Qinghai Zhou;H. Tong
中科院分区：
文献类型：
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作者：
Jian Kang;Qinghai Zhou;H. Tong
图卷积网络（GCN）在许多实际应用中表现出很强的经验性能。绝大多数关于GCN的现有工作主要集中在准确性上，而忽略了GCN对其预测的信心或不确定性。尽管是可信图挖掘的基石，但GCN的不确定性量化尚未得到很好的研究，现有的努力要么无法提供确定性量化，要么必须通过引入额外的参数或架构来改变GCN的训练过程。在本文中，我们提出了第一种基于频率的GCN不确定性量化方法——JuryGCN，其核心思想是将节点的不确定性量化为一个折刀估计的置信区间宽度。此外，我们利用影响函数来估计GCN参数的变化，而无需重新训练以扩大计算量。提出的JuryGCN能够在不修改GCN架构或引入额外参数的情况下确定地量化不确定性。我们在主动学习和半监督节点分类任务中对真实数据集进行了广泛的实验评估，证明了所提出方法的有效性。
Graph Convolutional Network (GCN) has exhibited strong empirical performance in many real-world applications. The vast majority of existing works on GCN primarily focus on the accuracy while ignoring how confident or uncertain a GCN is with respect to its predictions. Despite being a cornerstone of trustworthy graph mining, uncertainty quantification on GCN has not been well studied and the scarce existing efforts either fail to provide deterministic quantification or have to change the training procedure of GCN by introducing additional parameters or architectures. In this paper, we propose the first frequentist-based approach named JuryGCN in quantifying the uncertainty of GCN, where the key idea is to quantify the uncertainty of a node as the width of confidence interval by a jackknife estimator. Moreover, we leverage the influence functions to estimate the change in GCN parameters without re-training to scale up the computation. The proposed JuryGCN is capable of quantifying uncertainty deterministically without modifying the GCN architecture or introducing additional parameters. We perform extensive experimental evaluation on real-world datasets in the tasks of both active learning and semi-supervised node classification, which demonstrate the efficacy of the proposed method.